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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...

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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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Inferring disease-related pathways using a probabilistic epistasis model.

P N Kanabar¹, C J Vaske, C H Yeang

¹Department of Biomolecular Engineering, University of California Santa Cruz, 1156 High Street, Santa Cruz, CA 95062, USA.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

|February 13, 2009

Summary

We developed a Joint Intervention Network (JIN) model to infer gene interactions. This method accurately identified new genes involved in Vibrio cholerae biofilm formation, improving upon existing techniques.

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Area of Science:

Microbiology
Systems Biology
Bioinformatics

Background:

Understanding gene regulatory networks is crucial for deciphering complex biological processes.
Vibrio cholerae's environmental persistence involves intricate regulatory mechanisms.
Existing methods for inferring gene interactions have limitations.

Purpose of the Study:

To present a novel probabilistic model, the Joint Intervention Network (JIN), for inferring gene regulatory interactions.
To apply JIN to the Vibrio cholerae regulatory network to uncover mechanisms of environmental persistence.
To identify novel genes involved in biofilm formation.

Main Methods:

Developed a probabilistic model (JIN) that uses gene expression changes from regulator knock-outs.
JIN accommodates various perturbation combinations (single, double, triple knock-outs).
Applied JIN to analyze expression data of 17 Vibrio cholerae biofilm indicator genes under knock-outs of three known regulators.

Main Results:

The Joint Intervention Network successfully inferred interactions within the Vibrio cholerae regulatory network.
The model identified new genes critical for Vibrio cholerae biofilm formation.
JIN demonstrated higher accuracy in identifying biofilm-related genes compared to expression profile clustering.

Conclusions:

The Joint Intervention Network is an effective tool for inferring gene regulatory interactions.
This approach provides novel insights into the mechanisms of Vibrio cholerae environmental persistence and biofilm formation.
JIN offers an improved method for discovering genes involved in complex biological processes.